Application of Deep Learning in Text Mining

DOI

10.2991/mce-14.2014.80How to use a DOI?

Keywords

deep learning; text mining; characteristics and applications; network technology; machine learning

Abstract

With the advancement of science and technology development and the development of social , more and more current information technology and computer technology has been used in the various aspects of life and work, the current rapid development of network technology has penetrated into all aspects of life. With the development of network technology and the text messaging of network gradually strengthened, wide and messy network information, in the vast network of information, how to efficiently access the information that we need quickly, which is related to the text mining technologies. The deep learning is a new learning method of the machine learning, it simulates the human brain and analysis the neural network through the imitation of the human brain and the interpretation of the relevant data. In the text mining, the application of the deep learning can be a very good text clustering and text classification, it is easy to find the desired text information, so the application of the deep learning plays an important role in the deep learning. Research for this paper analyzes the deep learning in text mining and the related content of knowledge.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haoriqin Wang
AU  - Mingyang Jiang
AU  - Jianhong Qi
AU  - Xinhong Zhang
AU  - Qinghu Wang
AU  - Yuxin Zhou
AU  - Mingyu Bai
AU  - Lisha Liu
AU  - Zhili Pei
PY  - 2014/03
DA  - 2014/03
TI  - Application of Deep Learning in Text Mining
BT  - Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 361
EP  - 364
SN  - 1951-6851
UR  - https://doi.org/10.2991/mce-14.2014.80
DO  - 10.2991/mce-14.2014.80
ID  - Wang2014/03
ER  -